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    Item type:Publication, Access status: without files ,
    DuRTL - information flow analysis tool for register transfer level hardware designs
    (IEEE, 2025-01)
    Schammer, Lutz
    ;
    Martino, Gianluca
    ;
    Fey, Görschwin
    In this tool paper, we present a design information flow analysis tool called DuRTL. DuRTL is an open-source hardware information flow analysis tool implemented in C++ that helps designers understand and comprehend unknown hardware designs written in Verilog and VHDL. DuRTL implements an approach for Hardware Information Flow Tracking based on a tagging mechanism where unique tags are injected into a hardware design. Each tag is associated with a signal at a specific time and identifies a flow of information during the traversal of the circuit. We explain the methodology and IFT implementation of DuRTL and some of the design choices made during the development. We also present some experiments that show the capabilities of DuRTL.
    Publicationtype: Conference Paper
      19
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    Item type:Publication, Access status: with files ,
    AI-driven anomaly detection in oscilloscope images for post-silicon validation
    (2025-01)
    Akash, Kowshic A.
    ;
    Wulf, Tobias
    ;
    Valentin, Torsten
    ;
    Geist, Alexander
    ;
    Kulau, Ulf
    In the post-silicon validation process, different functionalities and boundaries of a system-on-chip (SoC) are tested, generating a large amount of data in the form of oscilloscope images, trace data, and log files. Oscilloscope images are used to visualize and analyze the digital I/O signals and play a crucial role in detecting anomalies. However, the debugging process of the oscilloscope images requires a lot of manual data analysis, which is time-consuming, inefficient, costly, and prone to errors. This paper proposes an artificial intelligence (AI) model to automatically detect anomalies in the oscilloscope images. Our proposed model uses a Convolutional Autoencoder (CAE), a neural network, which we train on real silicon data obtained from various post-silicon validation projects. While autoencoders have been used for anomaly detection, this is the first use to detect anomalies in oscilloscope images for post-silicon validation. Moreover, the state-of-the-art techniques use Reconstruction Error (RCE) as an anomaly detection metric, we show that a combination of RCE and Kernel Density Estimation (KDE) error metrics greatly reduces the false negatives (68%) for the anomalous category and improves the recall metric from 62% to 88%, making our approach 41% better. In addition, our proposed model achieves 99% precision in categorizing not anomalous data points. Furthermore, the proposed model has been deployed in the production environment, significantly reducing human effort.
    Publicationtype: Conference Paper
      89  94